I am using the observation with both leaf and flower observations

temporal_spatial gradient

source("plot_temporal_spatial_northredoak.R")
temporal_spatial

rm(list = ls())

data map for north red oak

source("plot_data_map.R")
data_map

rm(list = ls())

all species summary

source("table_spatial_temporal_statistics.R")
species_summary

plotly::ggplotly(species_summary)

compare the datasets in npn

path_npn <- "/nfs/turbo/seas-zhukai/phenology/NPN/individual_phenometrics/leaf_flower/with_climate/"
npn <- read_rds(stringr::str_c(path_npn, "Quercus", ".rds"))

npn %>%
  group_by(dataset_id) %>% 
  summarise(count = n())
## # A tibble: 13 × 2
##    dataset_id count
##    <chr>      <int>
##  1 '-9999'    25674
##  2 '-9999,16'    11
##  3 '-9999,3'   1810
##  4 '10'          25
##  5 '10,-9999'     1
##  6 '10,3'         2
##  7 '11'          51
##  8 '13'         273
##  9 '15'          73
## 10 '16'        7888
## 11 '16,-9999'   192
## 12 '3'        17252
## 13 '3,-9999'   1827
npn %>%
  group_by(npn$partner_group) %>% 
  summarise(count = n()) %>% 
  arrange(desc(count))
## # A tibble: 186 × 2
##    `npn$partner_group`                            count
##    <chr>                                          <int>
##  1 -9999                                          13842
##  2 National Ecological Observatory Network (NEON)  8112
##  3 Santa Monica Mountains NRA                      4191
##  4 Great Smoky Mountains NP                        2469
##  5 University at Buffalo                           1630
##  6 New York Botanical Garden Forest Phenology      1586
##  7 Sedgwick Reserve                                1562
##  8 John Muir NHS                                   1330
##  9 Meredith College                                1058
## 10 Pepperwood Preserve                             1053
## # ℹ 176 more rows